PPT-Distribution Networks:

control and pricing Desmond Cai Caltech CS John Ledyard Caltech Ec Steven Low Caltech CS and EE With a lot of help from others at Caltech and Southern California Edison. Two examples of network flow between cities in the US: internet connectivity (top), and recorded business travel flow (bottom). Case Study - Box 14.2. OBJECTIVES. Demonstrate the distinctions between local, national, regional and world cities in the urban . First half based on slides by . Kentaro Toyama,. Microsoft Research, India. And their applications to Web. Networks—Physical & Cyber. Typhoid Mary. (Mary Mallon). Patient Zero. (Gaetan Dugas). Applications of Network Theory. Dmitri Krioukov. CAIDA/UCSD. M. . . Á. . Serrano, M. . Bogu. ñá. . UNT, March 2011. Percolation. Percolation is one of the most fundamental and best-studied critical phenomena in nature. In networks: the critical parameter is often average degree . TJTSD66: Advanced Topics in Social Media. (Social . Media . Mining). Dr. WANG, Shuaiqiang @ CS & IS, JYU. Email: . shuaiqiang.wang@jyu.fi. Homepage: . http://users.jyu.fi/~swang/. Why should I use network models?. Units. IEOR 8100.003 Final Project. 9. th. May 2012. Daniel Guetta. Joint work with Carri Chan. This talk. Hospitals. Bayesian Networks. Data!. Modified EM Algorithm. First results. Instrumental variables. Learning Problem. Set of random variables . X. = {W, X, Y, Z, …}. Training set D = {. x. 1. , . x. 2. , …, . x. N. }. Each observation specifies values of subset of variables. x. 1. = {w. 1. , x. Christian Sohler. joint work with Artur Czumaj and Pan Peng. Very. Large Networks. Examples. Social. . networks. The World Wide Web. Cocitation. . graphs. Coauthorship. . graphs. Data . size. GigaByte. Erdős-Rényi. Random model, . Watts-. Strogatz. Small-world, . Barabási. -Albert Preferential attachment, . Molloy-Reed . Configuration model . and . Gilbert . Random . G. eometric model. Excellence Through Knowledge. Brief survey on optimization landscape for neural networks. Rong Ge. Duke University. Non-convex optimization. Theory: NP-hard. Practice: simple algorithms(SGD). Difficulties. Saddle Points. High-order Saddles. Adapted from Chapter 1. Of. Lei Tang and . Huan. Liu’s Book. 1. Chapter 1, . Community Detection and Mining in Social Media.  Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . Social Media: . Chapter 1. 1. Chapter. 1, . Community Detection and Mining in Social Media.  Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . Traditional Media. Broadcast Media: One-to-Many. Communication Media: One-to-One. Monojit . Choudhury. Microsoft Research India. monojitc@microsoft.com. . . light. color. red. blue. blood. sky. heavy. weight. 100. 20. 1. NLP vs. Computational Linguistics. Computational Linguistics is the study of . IEOR 8100.003 Final Project. 9. th. May 2012. Daniel Guetta. Joint work with Carri Chan. This talk. Hospitals. Bayesian Networks. Data!. Modified EM Algorithm. First results. Instrumental variables. Mike Freedman. COS 461: Computer Networks. http://. www.cs.princeton.edu. /courses/archive/spr20/cos461/. Continuation of . Lec. 15. 2. HTTP xfer = single object. Web pages = many objects. nytimes.com.